Percentile values are an often used business metric, because they are not susceptible to inordinate influence exerted by extreme values. For example, if a company, providing a network-based product, sought to ensure that 95% of their customers were provided responsive service from the network-based product, they could monitor the ninety-fifth percentile product response time. Such a value would not be affected if a couple of users experienced an abnormally long product response time, so long as those response times comprised less than five percent of all of the response times collected. Conversely, a metric such as an average value, would be disproportionally affected by anomalous entries.
The use of percentile-based metrics can require the sorting of potentially large sets of data since, as will be known by those skilled in the art, the first step in calculating a percentile value is the ordering of the relevant set of data. As will also be known by those skilled in the art, sorting a data set, whether through conventional bubble sort, selection sort, or more optimized sorting algorithms, can be a computationally expensive process. The computational cost further increases if the data set is large and if the data set is often updated with new data entries. In fact, the addition of even one data entry can require the resorting of a set of data.